Annotated clinical case
Reviewed by the DataLaps editorial team · Updated 2026-07-11
A clinical case to which expert physicians have added labels, verified diagnoses or verdicts, ready to train or evaluate AI.
An annotated clinical case is the basic unit of medical AI training and evaluation: the description of a case — chief complaint, history, findings, tests — enriched with the labels, verified diagnoses or verdicts that one or several expert physicians have added. It is what turns a clinical history into an example a machine can learn from or be tested on.
The richness of the annotation determines its usefulness. A case may carry simply the correct diagnosis, or incorporate deeper layers: the differential diagnosis considered, the appropriate course of action, the red-flag signs, and even the degree of agreement among several physicians who evaluated it independently. The more structured and consensus-backed the annotation, the more valuable the case.
When these cases are properly de-identified — removing any data that identifies the patient — and accompanied by the signature of the physicians who reviewed them, they become a dual asset: training and validation material for the AI, and reference clinical content that can be shared and consulted with guarantees of quality and traceability.
How much does it pay?
Contributing your judgment to the annotation of clinical cases is an expert task whose pay is set by each platform according to complexity and specialty. The value lies in the scarcity of verified medical judgment.
DataLaps does not advertise rates or an operational payment method today. What you build is a signed history of annotated cases that backs your clinical judgment.
How to get started
Annotating and issuing verdicts on real clinical cases is the direct way to contribute — and to prove your judgment — as a medical AI trainer.
Related terms
Sources
Start here
Train medical AI
Start validating cases
Sign up as a physician, validate real clinical cases and build your verifiable track record as a medical AI trainer.
Explore
Progress Library
Browse clinical syntheses reviewed by verified physicians: expert judgment put to work.
Transparency
How we work
Who is behind it, how we produce the content, and how we disclose the use of AI and human medical review.
Informational and educational content about the work of training and validating medical artificial intelligence. It does not constitute medical advice, diagnosis or treatment, nor an offer of employment or specific compensation.